content/computer_vision.json

{
    "heading": "Computer Vision",
    "intro": "I'm passionate about developing computer vision systems that can perceive and understand visual information in ways that benefit humans. My experience spans from implementing state-of-the-art algorithms to deploying them in real-world scenarios. I've worked on projects that enable machines to \"see\" and interpret their environment through image processing, object detection, and image classification. I focus particularly on applications that improve accessibility and solve tangible problems, creating CV solutions that operate efficiently even with hardware constraints.",
    "skills": [
      {
        "category": "CV Techniques",
        "items": ["Object Detection", "Image Segmentation", "Feature Extraction", "Image Classification"]
      },
      {
        "category": "CV Libraries",
        "items": ["OpenCV", "PIL/Pillow", "TorchVision", "TF Computer Vision"]
      },
      {
        "category": "Deep Learning for CV",
        "items": ["CNNs", "YOLO frameworks", "Transfer Learning", "Object Recognition"]
      },
      {
        "category": "Applications",
        "items": ["Accessibility Solutions", "OCR/Document Analysis", "Motion Tracking", "Edge Deployment"]
      }
    ],
    "projects": [
      {
        "title": "Smart Shopping Assistant for the Blind",
        "url": "https://github.com/Manyue-datascientist/smart_glove_project",
        "description": "Designed a system using object detection and OCR to help visually impaired individuals find products and navigate shopping aisles. Developed with real-time feedback on Raspberry Pi and OAK-D camera, this project demonstrates my commitment to creating technology that solves real accessibility challenges.",
        "tech_stack": "YOLOv8, OpenCV, Raspberry Pi"
      },
      {
        "title": "Traffic Flow Counter (Upcoming)",
        "url": "#",
        "description": "An edge solution using Raspberry Pi to monitor and count vehicles at intersections, providing real-time traffic flow analytics. This project demonstrates efficient deployment of CV models on resource-constrained devices.",
        "tech_stack": "YOLOv5, Raspberry Pi, OpenCV"
      }
    ]
  }